SoroushVahidi/scidocs huggingface dataset:
Probabilistic Extraction of Beat Positions from a Beat Activation Function
(2014)

We present a probabilistic way to extract beat positions from the output (activations) of the neural network that is at the heart of an existing beat tracker. The method can serve as a replacement for the greedy search the beat tracker currently uses for this purpose. Our experiments show improvement upon the current method for a variety of data sets and quality measures, as well as better results compared to other state-of-the-art algorithms.
Learning diverse rankings with multi-armed bandits
(2008)

Algorithms for learning to rank Web documents usually assume a document's relevance is independent of other documents. This leads to learned ranking functions that produce rankings with redundant results. In contrast, user studies have shown that diversity at high ranks is often preferred. We present two online learning algorithms that directly learn a diverse ranking of documents based on users' clicking behavior. We show that these algorithms minimize abandonment, or alternatively, maximize the probability that a relevant document is found in the top k positions of a ranking. Moreover, one of our algorithms asymptotically achieves optimal worst-case performance even if users' interests change.
Review of statistical shape spaces for 3D data with comparative analysis for human faces
(2014)

With systems for acquiring 3D surface data being evermore commonplace, it has become important to reliably extract specific shapes from the acquired data. In the presence of noise and occlusions, this can be done through the use of statistical shape models, which are learned from databases of clean examples of the shape in question. In this paper, we review, analyze and compare different statistical models: from those that analyze the variation in geometry globally to those that analyze the variation in geometry locally. We first review how different types of models have been used in the literature, then proceed to define the models and analyze them theoretically, in terms of both their statistical and computational aspects. We then perform extensive experimental comparison on the task of model fitting, and give intuition about which type of model is better for a few applications. Due to the wide availability of databases of high-quality data, we use the human face as the specific shape we wish to extract from corrupted data.
Positive affect facilitates creative problem solving.
(1987)

Four experiments indicated that positive affect, induced by means of seeing a few minutes of a comedy film or by means of receiving a small bag of candy, improved performance on two tasks that are generally regarded as requiring creative ingenuity: Duncker's (1945) candle task and M. T. Mednick, S. A. Mednick, and E. V. Mednick's (1964) Remote Associates Test. One condition in which negative affect was induced and two in which subjects engaged in physical exercise (intended to represent affectless arousal) failed to produce comparable improvements in creative performance. The influence of positive affect on creativity was discussed in terms of a broader theory of the impact of positive affect on cognitive organization.
On the bipolarity of positive and negative affect.
(1999)

Is positive affect (PA) the bipolar opposite of, or is it independent of, negative affect (NA)? Previous analyses of this vexing question have generally labored under the false assumption that bipolarity predicts an invariant latent correlation between PA and NA. The predicted correlation varies with time frame, response format, and items selected to define PA and NA. The observed correlation also varies with errors inherent in measurement. When the actual predictions of a bipolar model are considered and error is taken into account, there is little evidence for independence of what were traditionally thought opposites. Bipolarity provides a parsimonious fit to existing data.
Culture and the categorization of emotions.
(1991)

Some writers assume--and others deny--that all human beings distinguish emotions from nonemotions and divide the emotions into happiness, anger, fear, and so on. A review of ethnographic and cross-cultural studies on (a) emotion lexicons, (b) the emotions inferred from facial expressions, and (c) dimensions implicit in comparative judgments of emotions indicated both similarities and differences in how the emotions are categorized in different languages and cultures. Five hypotheses are reviewed: (a) Basic categories of emotion are pancultural, subordinate categories culture specific; (b) emotional focal points are pancultural, boundaries culture specific; (c) emotion categories evolved from a single primitive category of physiological arousal; (d) most emotion categories are culture specific but can be defined by pancultural semantic primitives; and (e) an emotion category is a script with both culture-specific and pancultural components.
The associative basis of the creative process.
(1962)

The intent of this paper is the presentation of an associative interpretation of the process of creative thinking. The explanation is not directed to any specific field of application such as art or science but attempts to delineate processes that underlie all creative thought. The discussion will take the following form, (a) First, we will define creative thinking in associative terms and indicate three ways in which creative solutions may be achieved—serendipity, similarity, and mediation, (b) This definition will allow us to deduce those individual difference variables which will facilitate creative performance, (c) Consideration of the definition of the creative process has suggested an operational statement of the definition in the form of a test. The test will be briefly described along with some preliminary research results. (d) The paper will conclude with a discussion of predictions regarding the influence of certain experimentally manipulable variables upon the creative process. Creative individuals and the processes by which they manifest their creativity have excited a good deal of
Missing data correction in still images and image sequences
(2002)

The ability to replace missing data in images and video is of key importance to many application fields. The general-purpose algorithm presented here is inspired by texture synthesis techniques but is suited to any complex natural scene and not restricted to stationary patterns. It has the property to be adapted to both still images and image sequences and to incorporate temporal information when available while preserving the simplicity of the algorithm. This method gives very good results in various situations without user intervention. The resulting computational cost is relatively low and corrections are usually produced within seconds.
Towards Wifi Mobility without Fast Handover
(2015)

WiFi is emerging as the preferred connectivity solution for mobile clients because of its low power consumption and high capacity. Dense urban WiFi deployments cover entire central areas, but the one thing missing is a seamless mobile experience. Mobility in WiFi has traditionally pursued fast handover, but we argue that this is the wrong goal to begin with. Instead, we propose that mobile clients should connect to all the access points they see, and split traffic over them with the newly deployed MPTCP protocol. We let a mobile connect to multiple APs on the same channel, or on different channels, and show via detailed experiments and simulation that this solution greatly enhances capacity and reliability of TCP connections straight away for certain flavors of WiFi a/b/g. We also find there are situations where connecting to multiple APs severely decreases throughput, and propose a series of client-side changes that make this solution robust across a wide range of scenarios.
Modeling 2D Appearance Evolution for 3D Object Categorization
(2016)

3D object categorization is a non-trivial task in computer vision encompassing many real-world applications. We pose the problem of categorizing 3D polygon meshes as learning appearance evolution from multi-view 2D images. Given a corpus of 3D polygon meshes, we first render the corresponding RGB and depth images from multiple viewpoints on a uniform sphere. Using rank pooling, we propose two methods to learn the appearance evolution of the 2D views. Firstly, we train view-invariant models based on a deep convolutional neural network (CNN) using the rendered RGB-D images and learn to rank the first fully connected layer activations and, therefore, capture the evolution of these extracted features. The parameters learned during this process are used as the 3D shape representations. In the second method, we learn the aggregation of the views from the outset by employing the ranking machine to the rendered RGB- D images directly, which produces aggregated 2D images which we term as ``3D shape images". We then learn CNN models on this novel shape representation for both RGB and depth which encode salient geometrical structure of the polygon. Experiments on the ModelNet40 and ModelNet10 datasets show that the proposed method consistently outperforms existing state-of-the-art algorithms in 3D shape recognition.
VMCTune: A Load Balancing Scheme for Virtual Machine Cluster Using Dynamic Resource Allocation
(2010)

This paper designs and implements a load balancing scheme based on dynamic resource allocation policy for virtual machine cluster, which are running under para-virtualization mode on a cluster of physical machines (PM) in shared storage architecture. It monitors the real-time resources utilization of VMs and PMs, including CPU, memory and network, then uses instant resource reallocation for virtual machines (VM) running on same PM to achieve local VMs’ load balancing, while uses live migration of VMs among PMs to achieve global VMs’ load balancing. It optimize the resource allocation of VMs to achieve global load balancing of virtual machine cluster. The resource utilization of physical machines will be improved as well. Compared to traditional load balancing schemes based on task scheduling, it is application independent and works seamless on VMs hosting different kinds of applications.
A Survey of Personality Computing
(2014)

Personality is a psychological construct aimed at explaining the wide variety of human behaviors in terms of a few, stable and measurable individual characteristics. In this respect, any technology involving understanding, prediction and synthesis of human behavior is likely to benefit from Personality Computing approaches, i.e. from technologies capable of dealing with human personality. This paper is a survey of such technologies and it aims at providing not only a solid knowledge base about the state-of-the-art, but also a conceptual model underlying the three main problems addressed in the literature, namely Automatic Personality Recognition (inference of the true personality of an individual from behavioral evidence), Automatic Personality Perception (inference of personality others attribute to an individual based on her observable behavior) and Automatic Personality Synthesis (generation of artificial personalities via embodied agents). Furthermore, the article highlights the issues still open in the field and identifies potential application areas.
A method for business process reengineering based on enterprise ontology
(2015)

Business Process Reengineering increases enterprise's chance to survive in competition among organizations , but failure rate among reengineering efforts is high, so new methods that decrease failure, are needed, in this paper a business process reengineering method is presented that uses Enterprise Ontology for modelling the current system and its goal is to improve analysing current system and decreasing failure rate of BPR, and cost and time of performing processes, In this method instead of just modelling processes, processes with their : interactions and relations, environment, staffs and customers will be modelled in enterprise ontology. Also in choosing processes for reengineering step, after choosing them, processes which, according to the enterprise ontology, has the most connection with the chosen ones, will also be chosen to reengineer, finally this method is implemented on a company and As-Is and To-Be processes are simulated and compared by ARIS tools, Report and Simulation Experiment
Analyzing Big Data in Psychology: A Split/Analyze/Meta-Analyze Approach
(2016)

Big data is a field that has traditionally been dominated by disciplines such as computer science and business, where mainly data-driven analyses have been performed. Psychology, a discipline in which a strong emphasis is placed on behavioral theories and empirical research, has the potential to contribute greatly to the big data movement. However, one challenge to psychologists-and probably the most crucial one-is that most researchers may not have the necessary programming and computational skills to analyze big data. In this study we argue that psychologists can also conduct big data research and that, rather than trying to acquire new programming and computational skills, they should focus on their strengths, such as performing psychometric analyses and testing theories using multivariate analyses to explain phenomena. We propose a split/analyze/meta-analyze approach that allows psychologists to easily analyze big data. Two real datasets are used to demonstrate the proposed procedures in R. A new research agenda related to the analysis of big data in psychology is outlined at the end of the study.
Compositional Analysis Framework Using EDP Resource Models
(2007)

Compositional schedulability analysis of hierarchical scheduling frameworks is a well studied problem, as it has wide-ranging applications in the embedded systems domain. Several techniques, such as periodic resource model based abstraction and composition, have been proposed for this problem. However these frameworks are sub-optimal because they incur bandwidth overhead. In this work, we introduce the explicit deadline periodic (EDP) resource model, and present compositional analysis techniques under EDF and DM. We show that these techniques are bandwidth optimal, in that they do not incur any bandwidth overhead in abstraction or composition. Hence, this framework is more efficient when compared to existing approaches.
Secure Overlay Cloud Storage with Access Control and Assured Deletion
(2012)

We can now outsource data backups off-site to third-party cloud storage services so as to reduce data management costs. However, we must provide security guarantees for the outsourced data, which is now maintained by third parties. We design and implement FADE, a secure overlay cloud storage system that achieves fine-grained, policy-based access control and file assured deletion. It associates outsourced files with file access policies, and assuredly deletes files to make them unrecoverable to anyone upon revocations of file access policies. To achieve such security goals, FADE is built upon a set of cryptographic key operations that are self-maintained by a quorum of key managers that are independent of third-party clouds. In particular, FADE acts as an overlay system that works seamlessly atop today's cloud storage services. We implement a proof-of-concept prototype of FADE atop Amazon S3, one of today's cloud storage services. We conduct extensive empirical studies, and demonstrate that FADE provides security protection for outsourced data, while introducing only minimal performance and monetary cost overhead. Our work provides insights of how to incorporate value-added security features into today's cloud storage services.
Object Discovery in Videos as Foreground Motion Clustering
(2018)

We consider the problem of providing dense segmentation masks for object discovery in videos. We formulate the object discovery problem as foreground motion clustering, where the goal is to cluster foreground pixels in videos into different objects. We introduce a novel pixel-trajectory recurrent neural network that learns feature embeddings of foreground pixel trajectories linked in time. By clustering the pixel trajectories using the learned feature embeddings, our method establishes correspondences between foreground object masks across video frames. To demonstrate the effectiveness of our framework for object discovery, we conduct experiments on commonly used datasets for motion segmentation, where we achieve state-of-the-art performance.
Learning to detect and localize many objects from few examples
(2016)

The current trend in object detection and localization is to learn predictions with high capacity deep neural networks trained on a very large amount of annotated data and using a high amount of processing power. In this work, we propose a new neural model which directly predicts bounding box coordinates. The particularity of our contribution lies in the local computations of predictions with a new form of local parameter sharing which keeps the overall amount of trainable parameters low. Key components of the model are spatial 2D-LSTM recurrent layers which convey contextual information between the regions of the image. We show that this model is more powerful than the state of the art in applications where training data is not as abundant as in the classical configuration of natural images and Imagenet/Pascal VOC tasks. We particularly target the detection of text in document images, but our method is not limited to this setting. The proposed model also facilitates the detection of many objects in a single image and can deal with inputs of variable sizes without resizing.
Brain activation modulated by sentence comprehension.
(1996)

The comprehension of visually presented sentences produces brain activation that increases with the linguistic complexity of the sentence. The volume of neural tissue activated (number of voxels) during sentence comprehension was measured with echo-planar functional magnetic resonance imaging. The modulation of the volume of activation by sentence complexity was observed in a network of four areas: the classical left-hemisphere language areas (the left laterosuperior temporal cortex, or Wernicke's area, and the left inferior frontal gyrus, or Broca's area) and their homologous right-hemisphere areas, although the right areas had much smaller volumes of activation than did the left areas. These findings generally indicate that the amount of neural activity that a given cognitive process engenders is dependent on the computational demand that the task imposes.
Power Waves and the Scattering Matrix
(1997)

This paper discusses the physical meaning and properties of the waves defined by v%+ z%Ib ~ = V. – Z,*Ii a%= ,, 2u/Re Z,] 2<1 ReZtl where V, and 1, are the voltage at and the current flowing into the ith port of a junction and Z, is the impedance of the circuit connected to the ith port. The square of the magnitude of these waves is directly related to the exchangeable power of a source and the reflected power. For this reason, in this paper, they are called the power waves. For certain applications where the power relations are of main concern, the power waves are more suitable quantities than the conventional traveling waves. The lossless and reciprocal conditions as well as the frequency characteristics of the scattering matrix are presented. Then, the formula is given for a new scattering matrix when the 2,’s are changed. As an application, the condition under which an amplifier can be matched simultaneously at both input and output ports as well as the condition for the network to be unconditionally stable are given in terms of the scattering matrix components. Also a brief comparison is made between the traveling waves and the power waves.
SKEE: A lightweight Secure Kernel-level Execution Environment for ARM
(2016)

Previous research on kernel monitoring and protection widely relies on higher privileged system components, such as hardware virtualization extensions, to isolate security tools from potential kernel attacks. These approaches increase both the maintenance effort and the code base size of privileged system components, which consequently increases the risk of having security vulnerabilities. SKEE, which stands for Secure Kernellevel Execution Environment, solves this fundamental problem. SKEE is a novel system that provides an isolated lightweight execution environment at the same privilege level of the kernel. SKEE is designed for commodity ARM platforms. Its main goal is to allow secure monitoring and protection of the kernel without active involvement of higher privileged software. SKEE provides a set of novel techniques to guarantee isolation. It creates a protected address space that is not accessible to the kernel, which is challenging to achieve when both the kernel and the isolated environment share the same privilege level. SKEE solves this challenge by preventing the kernel from managing its own memory translation tables. Hence, the kernel is forced to switch to SKEE to modify the system’s memory layout. In turn, SKEE verifies that the requested modification does not compromise the isolation of the protected address space. Switching from the OS kernel to SKEE exclusively passes through a well-controlled switch gate. This switch gate is carefully designed so that its execution sequence is atomic and deterministic. These properties combined guarantee that a potentially compromised kernel cannot exploit the switching sequence to compromise the isolation. If the kernel attempts to violate these properties, it will only cause the system to fail without exposing the protected address space. SKEE exclusively controls access permissions of the entire OS memory. Hence, it prevents attacks that attempt to inject unverified code into the kernel. Moreover, it can be easily extended to intercept other system events in order to support various intrusion detection and integrity verification tools. This paper presents a SKEE prototype that runs on both 32-bit ARMv7 and 64-bit ARMv8 architectures. Performance evaluation results demonstrate that SKEE is a practical solution for real world systems. 1These authors contributed equally to this work
Transparent Queries: Investigating Users' Mental Models of Search Engines
(2001)

Typically, commercial Web search engines provide very little feedback to the user concerning how a particular query is processed and interpreted. Specifically, they apply key query transformations without the users knowledge. Although these transformations have a pronounced effect on query results, users have very few resources for recognizing their existence and understanding their practical importance. We conducted a user study to gain a better understanding of users knowledge of and reactions to the operation of several query transformations that web search engines automatically employ. Additionally, we developed and evaluated Transparent Queries, a software system designed to provide users with lightweight feedback about opaque query transformations. The results of the study suggest that users do indeed have difficulties understanding the operation of query transformations without additional assistance. Finally, although transparency is helpful and valuable, interfaces that allow direct control of query transformations might ultimately be more helpful for end-users.